Economy-wide evaluation of CO2 and air quality impacts of electrification in the United States

Adopting electric end-use technologies instead of fossil-fueled alternatives, known as electrification, is an important economy-wide decarbonization strategy that also reduces criteria pollutant emissions and improves air quality. In this study, we evaluate CO2 and air quality co-benefits of electrification scenarios by linking a detailed energy systems model and a full-form photochemical air quality model in the United States. We find that electrification can substantially lower CO2 and improve air quality and that decarbonization policy can amplify these trends, which yield immediate and localized benefits. In particular, transport electrification can improve ozone and fine particulate matter (PM2.5), though the magnitude of changes varies regionally. However, growing activity from non-energy-related PM2.5 sources—such as fugitive dust and agricultural emissions—can offset electrification benefits, suggesting that additional measures beyond CO2 policy and electrification are needed to meet air quality goals. We illustrate how commonly used marginal emissions approaches systematically underestimate reductions from electrification.


Overview of Methods
EPRI's U.S. Regional Economy, Greenhouse Gas, and Energy (US-REGEN) model finds electric sector and end-use pathways that meet demand given assumptions about policies, technologies, and markets. Regional aggregation for this analysis is (Supplementary Figure 1).

Supplementary Figure 1. Regional aggregation of US-REGEN for this analysis.
The electricity model includes endogenous capacity planning and dispatch with joint investment decisions for generation options, energy storage technologies, transmission, hydrogen production, and CO2 removal, storage, and pipelines. Model decisions are made with perfect foresight.
The electric sector capacity expansion model is integrated with a technologically detailed consumer choice model of end-use service and energy demand, with the same geographic disaggregation and hourly temporal resolution. The end-use model represents the economic adoption of end-use technologies and fuels, particularly with respect to tradeoffs between electric and non-electric technologies. It includes a logit-based formulation for buildings and personal light-duty vehicle sectors to reflect behavioral heterogeneity by individual consumers in response to assumed total service costs of alternative technologies. In these sectors, the logit formulation is intended to represent behavioral heterogeneity rather than pure optimization. The market allocation in the logit model is still driven by assumed costs: The least-cost option does not capture 100% of the market allocation, but adoption rates are greater for technologies with greater cost advantage. The parameterization of the logit model determines the sensitivity of the market allocation (within a given nest in the nested structure) to assumed ("observed") costs, or conversely the influence of non-modeled ("non-observed") or costs or preferences, which by construction are assumed to be independently and identically distributed for each technology in the nest. In the case of the buildings sectors, the model is primarily evaluating tradeoffs between electric heat pumps, electric resistance, and gas (or other non-electric) technologies. While the cost and performance of these technologies may evolve over time, they all exist today and have observable market shares that vary across regions of the U.S. and segments of the building stock. Thus, the choice of logit model parameters, which are not associated with technologies per se but rather with nests defining groups of technologies among which market share is allocated, can be informed by currently observed patterns.
In the case of light-duty vehicles, until very recently there has been essentially one technology deployed, namely the internal combustion engine vehicle (ICEV). With the rapid development and commercialization of electric vehicles over the past several years, adoption rates are growing, but current market shares cannot be construed as representing an equilibrium allocation given the very early stage of development and remaining barriers to scale. For this reason, the logit parameters in this light-duty vehicle sector are not informed by current data but are chosen to represent a hypothetical long-run equilibrium. Moreover, the model includes a lagged function assigned market allocation in a given time step as a linear combination of the logit model's allocation based on current costs and the previous time step's actual allocation. Thus, there is a gradual convergence to the outcomes of the logit formulation when those outcomes are significantly different than the current outcome. In practice, the modeled total costs of electric vehicles suggest a significant cost advantage over ICEVs even in the near-term, with the advantage increasing over time as battery costs fall further and potential policy incentives increase the relative costs of non-electric fuels. The result is increasing adoption based on economic fundamentals, the pace of which is not strongly impacted by the choice of logit parameters. More information about the formulation and the parameter choices is available in Chapter 3 of the model documentation.
For other sectors, including medium-and heavy-duty transport and industrial activities, technology tradeoffs are based on an optimization formulation. In all sectors, technology adoption evolves over time based on economic fundamentals such as improvements in cost and performance and scenario-based policy incentives. The end-use model also includes the supply of non-electric fuels, including upstream oil and gas and petroleum refining, based on a simplified representation that captures costs, energy inputs, and emissions drivers. The electricity and end-use models are run iteratively, with the electric model passing hourly electricity prices to the end-use model, and the end-use model passing back hourly load shapes and load growth, until energy prices and demands converge between the two models (Supplementary Figure 2). The end-use model includes a separate module to evaluate rooftop solar PV adoption, investments in which are considered from the perspective of retail consumers, where installing behind-the-meter generation may offset retail purchases at flat volumetric rates. Rates are calculated using price outputs from the electric model and assume today's existing rate structures. The US-REGEN documentation Section 3.6 has more information (EPRI, 2020).

Supplementary
Electric vehicle charging shapes vary by household type, charging location, and day of the week and reflect a mix of charging locations (home, work, public) and power levels (Bistline, et al., 2021). Note that these shapes represent aggregate charging patterns rather than individual vehicle shapes. These charging profiles are based on research by EPRI's Electric Transportation program and come from a combination of aggregated empirical data and simulation. REGEN also provides options for flexible charging, including for a fleet of autonomous vehicles. The participation share in charging flexibility programs is assumed to be 50% for residential households and 80% for workplace charging. The flexible charging module in REGEN minimizes the cost of charging subject to constraints that the daily vehicle energy requirement for each profile must be met in the available hours and that charging in each hour is limited by available capacity. The focus of the charging module is load shifting, where vehicle charging shifts in time as response to utility control, time-varying pricing, or other incentives.
Hourly profiles used in the model solution are based on a single representative year (2015 for these experiments), and the same underlying meteorology and temperatures are used in the end-use model to develop hourly load shapes (e.g., for electric space heating in residential and commercial buildings) to avoid dampening variance through multi-year averaging. While consideration of multiple weather years may reveal more extreme events, there are significant wind droughts observed in the sample year, reinforcing the importance of energy storage and firm resources for balancing. Moreover, the model includes a reserve requirement that firm capacity exceed the residual peak load in each region, suggesting the results are relatively robust to extended wind and solar droughts. Additional detail on wind and solar resource assumptions and technology characteristics is provided in Section 2.4 of the REGEN documentation (EPRI, 2020). US-REGEN future-year emissions were transformed into emission inventory inputs for use in air quality modeling. US-REGEN sector, source, fuel type, and region were cross-referenced to source category classification code (SCC) and county for nonpoint sources and SCC, county, and North American Industry Classification System (NAICS) for point sources. US-REGEN emissions were then mapped into air quality modeling format based on 2014 NEI emissions. For US-REGEN emissions that comprised multiple 2014 NEI categories, emissions were disaggregated in accordance with 2014 NEI distributions (e.g., by county) while US-REGEN emission totals were retained (e.g., statewide).

Development of Emissions Input for Air Quality Modeling
Future-year emissions were further adjusted to improve consistency with the 2016 baseline (2016 EI). Although the EPA's 2014 (US-REGEN emission baseline) and 2016 EI (air quality modeling baseline) are mostly comparable, there are notable differences in oil & gas, commercial marine vessel Class 1&2 and Class 3, pleasure craft, and rail due to changing methodology or underlying data. For these sectors, US-REGEN "forecast factors" (e.g., the relative changes in emissions from 2035 vs 2015) were applied to the 2016 EI. Certain non-electrified sources including fugitive dust (except for paved road dust), residential wood combustion, and agricultural burning are held constant from the 2016 EI with an assumption that future controls will offset growth.
The US-REGEN emissions were processed to develop more temporally, spatially, and chemically refined inventory inputs required for air quality modeling using the SMOKE system 3 . The emissions were processed by EPA standard source categorization, i.e., emission sector. This is done to assist in the quality assurance of the emissions modeling by comparing emissions developed in this study with the EPA's 2016 EI: • Agricultural: Agricultural fertilizer and livestock emissions • Residential wood combustion (RWC): Residential wood-burning devices such as fireplaces and wood stoves • Oil & Gas: Oil and gas exploration and production sources • Fugitive dust: Fugitive dust sources such as paved/unpaved road dust and construction dust Note that changes in the primary PM emissions in the other nonpoint emissions are driven in large part by changes in two activities: cooking and wood combustion. Cooking emissions increase steadily over time due to increases in service demand (even with electrification trends). Wood combustion changes from the Limited Electrification scenario to the High Electrification scenario without a carbon price, as there is less wood consumption due to increased electrification. However, when a carbon price is added, there is an increased use of biomass because it is a low-carbon fuel and appears relatively cheaper than electrification for certain households and climate zones. This effect is happening primarily in buildings due to space heating.

Supplementary Figure 5. Spatial distribution of VOC emissions (tons per year) for the CONUS and emissions difference map between Electrification with Carbon Price scenarios.
VOC emissions are largely driven by changes in four source categories: on-road vehicles, non-road sources, oil and gas activity, and other nonpoint sources. VOC emissions from on-road vehicles and nonroad sources decline due to fleet turnover in the 2035 Limited Electrification scenario, but they are offset by emission increases from oil and gas activity and other nonpoint sources, such as solvent utilization and commercial and residential fuel combustion. High Electrification without Carbon Price can further reduce VOC emissions from on-road and non-road mobile sources due to further levels of electrification. VOC emissions from oil and gas sources decline in the 2035 High Electrification with Carbon Price scenario due to the reduced activity in this sector.

Air Quality Modeling Configuration and Initialization
CAMx model configuration: CAMx version 7.0 is a 3-D photochemical transport and dispersion model that has an Eulerian (grid-based) formulation (www.camx.com). The key processes treated by CAMx are emission, transport and dispersion, atmospheric chemical transformation, and deposition to the earth's surface of trace gases and aerosols. CAMx was set up to use Carbon Bond 6 (CB6) gas phase chemistry and other common configurations listed in Supplementary Table 2. The CAMx modeling grids were the 12-km/36-km grids as used in the EPA's 2016 Modeling. The grids are defined using a Lambert-Conformal map projection (Alpha = 33º, Beta = 45º and Gamma = -97º, with a center of X = -97º and Y = 40º) with the southwest corner at (-2412 km, -1620 km). The 12-km domain covers the 48 contiguous states along with southern portions of Canada and northern portions of Mexico (Supplementary Figure 6) with 12-km grid resolution, and has 396 by 246 grid cells and 35 vertical layers. EPA evaluated surface winds, temperature, water vapor, and precipitation patterns against observations and found that overall performance was adequate although spatial and temporal variations in performance were noted. Using 12-km grid resolution tended to improve WRF performance over using 36-km grid resolution, and there was a tendency for better WRF performance in the Eastern U.S. than the Western U.S., which can be attributed to the complex terrain of the West. Running WRF and CAMx independently is efficient, because WRF is run only once, but has the limitation of omitting aerosol feedbacks to WRF meteorology although aerosol feedbacks to photolysis reactions are included within CAMx. Uncertainties in PM/O3 responses introduced by omitting this feedback are likely much smaller than other factors such as the magnitude of the emission changes.

Supplementary
An evaluation of the baseline air quality model performance is important to support conclusions drawn from the future-year scenarios. Ozone performance is evaluated against measurements obtained from the Clean Air Status and Trends Network (CASTNet) and Air Quality System (AQS). PM2.5 is evaluated against measurements obtained from the more rural IMPROVE and more urban Chemical Speciation Monitoring Network (CSN). The CAMx 2016 Baseline simulation shows reasonable performance for both ozone (Supplementary Table 3) and PM2.5 (Supplementary Table 4).

Additional Energy System Results
Electrification rates as a share of final energy for these scenarios in 2035 and 2050 generally align with recent economy-wide deep decarbonization and net-zero scenarios (Supplementary Figure 14). Electricity's share of final energy increases in the reference electrification scenario from 21% today to 43% by 2050 in the High Electrification scenario (54% with a carbon tax). Although electricity demand increases with higher end-use electrification, load growth exhibits a broader range. There are three interacting policy-induced effects that lead to differences in electricity demand across models, scenarios, and countries: • Policies such as CO2 taxes alter end-use energy prices, which influence fuel demand contingent on policy design and emissions intensities. • Electricity prices drop relative to fossil fuels due to the greater range of mitigation technologies in the power sector and their relatively low costs, which leads to electrification-the substitution of electricity for fossil fuel use. This leads to higher electricity demand. • Challenging-to-electrify end uses such as heavy industry and air travel could lead to indirect electrification through electrolytic hydrogen and synthetic hydrocarbon fuels that are derived from electricity. Such indirect electrification can increase load growth, especially since such fuel conversion pathways typically entail low roundtrip efficiencies. However, such alternative fuels face competition from direct electrification, alternative low-emitting fuel pathways (e.g., biofuels), and using fossil fuels with carbon removal. Supplementary Figure 16 compares the electric vehicle share of new sales across scenarios in this paper with ones from a recent multi-model study of reaching the 2030 U.S. climate target. 4 This comparison suggests that the High Electrification scenario is more consistent with current market trends that the Limited Electrification scenario, which is used as a baseline to illustrate the air quality impacts of electrification.